The present application relates to systems and methods for minimizing energy cost in response to time-varying pricing scenarios. The systems and methods described herein may be used for demand response in building or HVAC systems such as those sold by Johnson Controls, Inc.
The rates that energy providers charge for energy often vary throughout the day. For example, energy providers may use a rate structure that assigns different energy rates to on-peak, partial-peak, and off-peak time periods.
Additionally, energy providers often charge a fee known as a demand charge. A demand charge is a fee corresponding to the peak power (i.e. the rate of energy use) at any given time during a billing period. In a variable pricing scenario that has an on-peak, partial-peak, and off-peak time period, a customer is typically charged a separate demand charge for maximum power use during each pricing period.
Energy providers can also offer customers the option to participate in a critical-peak pricing (CPP) program. In a CPP program, certain days throughout a billing period are designated as CPP days. On a CPP day, the on-peak time period is often divided in two or more sub-periods. CPP periods may also have separate demand charges for each sub-period. As an incentive to participate in the CPP program, customers are charged a lower energy rate on non-CPP days during the billing period.
Energy providers also often engage in real-time pricing (RTP). RTP energy rates change frequently and can vary quite drastically throughout the day. RTP periods may also have a separate demand charge for each RTP period. It is challenging and difficult for energy customers would like to minimize the cost that they pay for energy where pricing scenarios can be mixed.
Control actions can be taken to respond to variable pricing scenarios. One response is to turn off equipment. However, when the energy is used to drive a heating or cooling system for a building, the cost minimization problem is often subject to constraints. For example, it is desirable to maintain the building temperature within an acceptable range. Methods that are more proactive include storing energy in batteries or using ice storage to meet the future cooling loads. A problem with many of these techniques is the requirement for large, expensive, and non-standard equipment.
A method that does not require additional equipment is storing energy in the thermal mass of the building. This form of thermal energy storage risks leading to either uncomfortable building zone temperatures or demand charges that are not significantly reduced. One technique is to pre-cool the building to a minimum allowable temperature and to determine the temperature setpoint trajectory that will minimize power use while maintaining the temperature below a maximum allowable value. With this technique, the demand can be curtailed and the zone temperature can remain within temperature comfort bounds.
Traditional methods are less than optimal and are unable to handle RTP pricing scenarios with rapidly changing energy prices or CPP pricing scenarios having several regions of interest for both energy and demand charges. Furthermore, traditional methods may have difficulty accounting for varying disturbances to the system or changes to the system which are likely to necessitate re-developing or retraining the underlying model.
Energy cost minimization systems and methods are needed to address a plurality of variable pricing schemes including the rapidly changing energy cost structures of CPP and RTP. Additionally, a method is needed which handles the possibility of multiple demand charge regions and which handles varying disturbances and changes to the system without the need to re-train the model.